AI/ML Systems Engineer

Role Overview

Build and optimize production systems for AI/ML training and inference pipelines that handle multimodal scientific data.

What You'll Do

• Design and implement scalable infrastructure for processing multimodal data from theoretical models, scientific literature, and experimental systems
• Develop efficient data pipelines for training and deploying AI models in a scientific discovery context
• Optimize model performance and resource utilization across diverse computational environments
• Collaborate with computational chemists to integrate domain-specific knowledge into data processing workflows

What You Bring

• Experience with ML operations (MLOps) and production AI systems
• Familiarity with frameworks like PyTorch, TensorFlow, or JAX
• Background in distributed computing and/or high-performance scientific computing
• Knowledge of containerization and orchestration tools (Docker, Kubernetes)

Nice to Have

• Experience with scientific computing libraries and frameworks
• Background in chemistry, materials science, or related fields
• Contributions to open-source ML infrastructure projects

Benefits

• Competitive salary and equity
• Comprehensive health, dental, and vision insurance
• Generous PTO and parental leave
• Opportunities for professional development